Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning

Paper describes a data-driven framework based on spatio-temporal ensemble machine learning to produce distribution maps for 16 forest tree species (Abies alba Mill., Castanea sativa Mill. , Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of 3 million of points was used to train different Machine Learning (ML) algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 585 coarse and high resolution covariates representing spectral reflectance (Landsat bands, spectral indices; time-series of seasonal composites), different biophysical conditions (i.e. temperature, precipitation, elevation, lithology) and biotic competition (other species distribution maps) was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to train an ensemble model based on stacking with a logistic regressor as a meta-learner for each species. High resolution (30 m) probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of 6 distribution maps per species for the studied period, while for potential distributions only one map per species was produced. Results of spatial cross validation show that Olea europaea and Quercus suber achieved the best performances in both potential and realized distribution, while Pinus sylvestris and Salix caprea achieved the worst. Further analysis shows that fine-resolution models consistently outperformed coarse resolution models (250 m) for realized distribution (average decrease in logloss: +53%). Realized distribution models achieved higher predictive performances than potential distribution ones. Importance of predictor variables differed across species and models, with the green band for summer and the NDWI and NDVI for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter being the most important and frequent for potential distribution. The ensemble model outperformed or performed as good as the best individual model in all potential species distributions, while for ten species it performed worse than the best individual model in modeling realized distributions. The framework shows how combining continuous and consistent EO time series data with state of the art ML can be used to derive dynamic distribution maps. The produced time-series occurrence predictions can be used to quantify temporal trends and detect potential forest degradation.

Preschool Children’s Speech Pedagogical Sound Culture Correction

Objective: The article aims to reveal the features of correction of the sound culture of the preschool-age children's speech, the effectiveness of which has been tested experimentally. Background: The sound culture of speech is a multicomponent formation, which covers the phonetic correctness of speech; general language skills and orthoepic correctness of speech. Pedagogical correction of the sound culture of speech is focused on the correct the errors caused by a violation of the sound articulation, sound pronunciation, orthoepic norms of pronunciation, voice strength, etc. Method: In the study, the author's method of pedagogical correction of the sound culture of children’s speech was used. Also, it was used comparative analysis and method of successive analysis of adjustment variants of the speech sound culture. Results: An individual model of pedagogical correction of the sound culture of the child's speech was developed. Training to deepen knowledge, improvement of abilities, and skills of teachers were held. The exercises in sound pronunciation and intonational speech expressiveness were developed. Conclusion: Positive dynamics of developmental levels of the sound culture of children’s speech, which has been confirmed by the results of quantitative and qualitative analysis, confirms the effectiveness of the experimental methods of pedagogical correction of the sound culture of speech.

An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

Explaining Regeneration: Cells and Limbs as Complex Living Systems, Learning From History

Regeneration has been investigated since Aristotle, giving rise to many ways of explaining what this process is and how it works. Current research focuses on gene expression and cell signaling of regeneration within individual model organisms. We tend to look to model organisms on the reasoning that because of evolution, information gained from other species must in some respect be generalizable. However, for all that we have uncovered about how regeneration works within individual organisms, we have yet to translate what we have gleaned into achieving the goal of regenerative medicine: to harness and enhance our own regenerative abilities. Turning to history may provide a crucial perspective in advancing us toward this goal. History gives perspective, allowing us to reflect on how our predecessors did their work and what assumptions they made, thus also revealing limitations. History, then, may show us how we can move from our current reductionist thinking focused on particular selected model organisms toward generalizations about this crucial process that operates across complex living systems and move closer to repairing our own damaged bodies.

Clinical Evaluation of Non-Contact Infrared Thermometers

Background:Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. Methods:To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. Results:The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under -0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from -3 °C to +2 °C in extreme cases, with the majority of the differences between -2 °C and +1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject’s temperature above 38 °C ranged from 0 to 0.69. Conclusions: Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject’s temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

Memelihara Kestabilan Emosional dengan Terapi Emotional Freedom Technique (EFT) di Masa Pandemi Covid–19

The pandemic of covid-19 has impacted many sectors. Millions of people victims this virus and disturb mental health. All of them becase declining income, social distancing and methods that always change. It’s need preventive efford and curative to save healty and emotional. Emotional freedom technique (EFT) is felt appropriately for stabilizing emotions and became simple methods to all generation. Focus on negative emotional problem that sould be addressed by describing problems and typing meridian. the purpose from This activities are for protect emotional stability by Emotional freedom technique. The method used by qualitative phenomology. The subject or client from this EFT is the youth of palembon village, kanor district in the bojonegoro regency. This activity is in the forum of workshop by nine youth of palembon in resident’s house at 9-10 of april 2021. From this workshop, subject get education about EFT and how to applicated it. In the first day, the workshop start from 09.00 AM until 02.00 PM. And in the second day, start from 09.00 AM. Until 12.00 AM. In the third day, any perform devotion to the subject with individual model to put more emphasis on aspects of decency and secrecy, that is on 12 – 13 of April 2021. The problem that arised in the post-crucial of the pandemic is boredom complained regarding education and employment. Use EFT make persons more relaxed and less nervous to do something. The result from this devotion is satisfying results. One use therpy of EFT in the a group and 3 times by personal make the client’s cycle of negative emotional problems has declined significantly.

Lightweight Convolutional Neural Networks with Model-Switching Architecture for Multi-Scenario Road Semantic Segmentation

A convolutional neural network (CNN) that was trained using datasets for multiple scenarios was proposed to facilitate real-time road semantic segmentation for various scenarios encountered in autonomous driving. However, the CNN inhibited the mutual suppression effect between weights; thus, it did not perform as well as a network that was trained using a single scenario. To address this limitation, we used a model-switching architecture in the network and maintained the optimal weights of each individual model which required considerable space and computation. We, subsequently, incorporated a lightweight process into the model to reduce the model size and computational load. The experimental results indicated that the proposed lightweight CNN with a model-switching architecture outperformed and was faster than the conventional methods across multiple scenarios in road semantic segmentation.

Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing

This study assessed multi-model subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multi-model ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill were relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multi-model ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2-4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3-4, while the ENSO phases influenced the skill mostly in the southern regions.